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All Journal ComTech: Computer, Mathematics and Engineering Applications
Harahap, Libelda Aldinaduma
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Computer Science & IT Engineering Mathematics

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Clustering Analysis of MAMA 2024 Song of the Year Nominees Based on Musical Elements and Popularity Indicators Harahap, Libelda Aldinaduma; Sofro, A'yunin
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12860

Abstract

As K-pop continues to dominate global music charts, understanding the factors behind the success of songs has become increasingly essential. This study explores how musical elements and popularity indicators reveal patterns among topperforming songs. A total of 57 songs nominated for the 2024 Song of the Year category were grouped using hierarchical cluster analysis. The genre variable was consolidated into six broader categories and converted into numerical labels. All variables are normalized using the Min-Max normalization method before clustering. The data includes musical elements such as genre, tempo, danceability, energy, and happiness, as well as popularity indicators like YouTube views and Spotify streams. The analysis employs single, complete, and average linkage methods. Among these, the average linkage method yields the best results, with an agglomerative coefficient value of 0.8167. Seven distinct clusters are identified: Cluster 1 features R&B and hip-hop styles with varied energy and rhythms; Cluster 2, the largest group, includes high-energy pop, hip-hop, and dance-pop tracks that are popular on streaming platforms; Cluster 3 contains indie and experimental tracks; Cluster 4 emphasizes high-energy stage performances; Cluster 5 is an outlier with experimental traits; Cluster 6 highlights R&B and funk with global appeal; and Cluster 7 includes emotional OSTs and ballads with slower tempos. By combining musical elements and popularity indicators, this research uncovers patterns of success in K-pop songs. These findings offer actionable insights for artists, producers, and marketers, providing a datadriven reference for creating music that resonates with modern audience preferences.
Co-Authors Sofro, A'yunin